Repository logoGCRIS
  • English
  • Türkçe
  • Русский
Log In
New user? Click here to register. Have you forgotten your password?
Home
Communities
Browse GCRIS
Entities
Overview
GCRIS Guide
  1. Home
  2. Browse by Author

Browsing by Author "Agirman, Ahmet K."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Results Per Page
  • Sort Options
  • Loading...
    Thumbnail Image
    Article
    Citation - WoS: 6
    Citation - Scopus: 7
    BLSTM Based Night-Time Wildfire Detection From Video
    (Public Library Science, 2022) Agirman, Ahmet K.; Tasdemir, Kasim
    Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behavior of night-time fire indispensable for classification. To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. Moreover, to pave the way for more targeted solutions to this challenging problem, experiment-based thorough investigations of possible sources of incorrect predictions and discussion of the unique nature of night-time wildfire videos are presented in the paper.
  • Loading...
    Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Kısa Ve Orta Mesafe Gece Yangını Tespiti
    (Institute of Electrical and Electronics Engineers Inc., 2017) Agirman, Ahmet K.; Taşdemir, Kasím
    Computer vision methods used for night-time fire detection are limited. Existing works are for detection of distant night fires recorded from watch towers. In this paper, detection of short to mid-range night fires from video cameras are aimed. Flames in short distance flicker, grow and move more rapidly compared to ones in long distance. Features obtained by taking advantage of these distinctions let us detect fire over 90% accuracy on average in videos containing deceptive light sources like common city lights and headlights of vehicles. © 2017 Elsevier B.V., All rights reserved.
Repository logo
Collections
  • Scopus Collection
  • WoS Collection
  • TrDizin Collection
  • PubMed Collection
Entities
  • Research Outputs
  • Organizations
  • Researchers
  • Projects
  • Awards
  • Equipments
  • Events
About
  • Contact
  • GCRIS
  • Research Ecosystems
  • Feedback
  • OAI-PMH

Log in to GCRIS Dashboard

Powered by Research Ecosystems

  • Privacy policy
  • End User Agreement
  • Feedback